Tidy Tuesday #5

library(tidyverse)     # for graphing and data cleaning
library(lubridate)     # for date manipulation
library(ggthemes)      # for even more plotting themes
#library(janitor)       # for cleaning variable names
theme_set(theme_minimal()) # My favorite ggplot() theme :)
library(treemapify)

# Read in the data for the week
employed <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-23/employed.csv')

earn <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-23/earn.csv')

In this week, I am interested in comparing the median weekly income of men and women in the last decade from 2010 to 2020. I decide to use geom_treemap and gganimate to create animation of a treemap that display the proportion of average median weekly income for each year and each ages group.

earn_clean <-  earn %>% 
  filter(sex !="Both Sexes", age %in% c("16 to 19 years", "20 to 24 years", "25 to 34 years", "35 to 44 years", "45 to 54 years","55 to 64 years", "65 years and over")) %>% 
  group_by(sex, age, year) %>% 
  summarise(avg_median_weekly_earn = ceiling(sum(median_weekly_earn)/4), label = paste( avg_median_weekly_earn ))
earn_clean

p <-
  earn_clean %>% 
  ggplot(aes(
    label = label,
    area = avg_median_weekly_earn,
    subgroup = sex,
    fill = age
  )) +
  geom_treemap(layout = "fixed") +
  geom_treemap_text(layout = "fixed", place = "centre", grow = TRUE ) +
  geom_treemap_subgroup_text(layout = "fixed", place = "centre", color = "white") +
  geom_treemap_subgroup_border(layout = "fixed", color="white")+
  theme(legend.position = "bottom")+
  transition_time(year) +
  ease_aes('linear') +
  labs(title = "Median Weekly Income Distrubution in Dollars", subtitle = "Year: {round(frame_time,0)}", caption = " by Vichearith Meas", fill="Age Groups")
#

animate(p, duration = 1, fps = 15)

anim_save("tidyTuesday5_treemap.gif")

knitr::include_graphics("tidyTuesday5_treemap.gif")